[MXFP4] Add bias to MXFP4 GEMM kernel #753
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Note Other AI code review bot(s) detectedCodeRabbit has detected other AI code review bot(s) in this pull request and will avoid duplicating their findings in the review comments. This may lead to a less comprehensive review. WalkthroughAdds optional bias support to the quantized GEMM example. Updates kernel signature and entry to accept a bias flag and bias tensor, allocates/copies bias to shared memory when enabled, initializes output from bias, and extends reference programs and the test harness to exercise bias and no-bias configurations. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant User
participant Main as main()
participant Kernel as matmul(...)
participant Shared as bias_shared
participant Out as C_local
User->>Main: call with m,n,k, fast_dequant, with_bias
Main->>Kernel: build/launch matmul(..., with_bias)
alt with_bias == true
Kernel->>Shared: allocate & copy bias
Shared-->>Kernel: bias tile ready
Kernel->>Out: initialize C_local from bias_shared
else with_bias == false
Kernel->>Out: initialize C_local to zeros
end
Kernel->>Kernel: dequantize B (if fast), compute A·Bᵀ accumulate into C_local
Kernel-->>Main: write C_local back to C
Main-->>User: return results (compared to bias-aware refs)
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes Possibly related PRs
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Summary of Changes
Hello @tzj-fxz, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request introduces the capability to add a bias term to the MXFP4 GEMM (General Matrix Multiply) kernel. This enhancement allows for more flexible and complete matrix multiplication operations, commonly used in neural networks, by incorporating an additive bias directly into the kernel's computation.
Highlights
- Bias Parameter Introduction: A new with_bias boolean parameter has been added to the matmul function and its main entry point, enabling users to control whether a bias is applied.
- Bias Tensor Integration: The kernel now accepts an additional bias tensor input, and corresponding shared memory allocations (bias_shared) are managed within the matmul implementation.
- In-Kernel Bias Application: Logic has been added to the matmul kernel to conditionally load and apply the bias tensor to the accumulator (C_local) before the main matrix multiplication loop begins, ensuring the bias is incorporated efficiently.
- Reference Program Updates: New reference implementations (ref_program_twiddling_with_bias, ref_program_simple_with_bias) have been introduced, and existing ones modified, to accurately reflect and validate the bias addition functionality.
- Expanded Test Coverage: The example's main execution block now includes test cases for both with_bias=True and with_bias=False scenarios, ensuring comprehensive validation of the new feature.
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Code Review
This pull request adds support for bias in the MXFP4 GEMM kernel. The changes correctly propagate the with_bias flag and add the necessary logic for handling the bias tensor in the kernel. The reference programs for validation have also been updated. My review focuses on improving the new reference functions by addressing code duplication, fixing a malformed docstring, and suggesting performance improvements through vectorization.
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| def ref_program_simple_with_bias(A, qB, Scale, bias): | ||
| """ | ||
| Compute a BF16 matrix product A · B^T from a quantized B with simple (non-twiddling) dequantization. | ||
| Converts the quantized tensor `qB` to floating B via `torch_convert`, applies a per-element scale factor computed as 2^(Scale[i][j//32] - 127) (Scale supplies exponent offsets in 32-column groups), then computes C = A · B^T and returns the result converted to bfloat16. | ||
| Parameters: | ||
| Returns: | ||
| - A: 2D tensor representing the left operand (will be cast to float32 for the matmul). | ||
| - qB: Quantized representation of B accepted by `torch_convert`. | ||
| - Scale: 2D tensor of exponent offsets; Scale[i][g] is applied to columns j where g == j // 32. | ||
| - bias: 2D tensor representing the bias (will be cast to float32 for the matmul). | ||
| Returns: | ||
| - 2D bfloat16 tensor C containing the matrix product A · B^T. | ||
| No in-place modification is performed on inputs (a local floating copy of B is scaled). | ||
| """ | ||
| dtypeC = "bfloat16" | ||
| B = torch_convert(qB) | ||
| for i in range(B.shape[0]): | ||
| for j in range(B.shape[1]): | ||
| B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) | ||
| C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias | ||
| C = C.to(torch.__getattribute__(dtypeC)) | ||
| return C |
There was a problem hiding this comment.
This function has a few issues that should be addressed:
- Malformed Docstring: The docstring is confusing as the parameters are listed under a
Returns:section, and there are twoReturns:sections. - Code Duplication: This function is very similar to
ref_program_simple. This code can be merged intoref_program_simpleby handling an optionalbiasparameter to improve maintainability. - Inefficient Scaling: The Python loops for scaling are inefficient. Vectorizing this with PyTorch operations will improve performance significantly.
Here is a suggestion that fixes the docstring and uses vectorized scaling. You can apply similar logic to merge this with ref_program_simple.
def ref_program_simple_with_bias(A, qB, Scale, bias):
"""Compute a BF16 matrix product A · B^T from a quantized B with simple (non-twiddling) dequantization.
Converts the quantized tensor `qB` to floating B via `torch_convert`, applies a per-element
scale factor computed as 2**(Scale[i][j//32]) (Scale supplies exponent offsets in 32-column
groups), then computes C = A · B^T + bias and returns the result converted to bfloat16.
Parameters:
A (torch.Tensor): 2D tensor representing the left operand (will be cast to float32 for the matmul).
qB (torch.Tensor): Quantized representation of B accepted by `torch_convert`.
Scale (torch.Tensor): 2D tensor of exponent offsets; Scale[i][g] is applied to columns j where g == j // 32.
bias (torch.Tensor): 2D tensor representing the bias (will be cast to float32 for the matmul).
Returns:
torch.Tensor: 2D bfloat16 tensor C containing the matrix product A · B^T.
No in-place modification is performed on inputs (a local floating copy of B is scaled).
"""
dtypeC = "bfloat16"
B = torch_convert(qB)
scale_expanded = Scale.repeat_interleave(32, dim=1)
if scale_expanded.shape[1] > B.shape[1]:
scale_expanded = scale_expanded[:, :B.shape[1]]
B *= (2**scale_expanded)
C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias
C = C.to(torch.__getattribute__(dtypeC))
return C
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| for i in range(B.shape[0]): | ||
| for j in range(B.shape[1]): | ||
| B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) |
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The Python loops for applying scales are inefficient. You can achieve a significant performance improvement by vectorizing this operation using PyTorch's tensor operations. This also makes the code more concise.
Also, consider merging this function with ref_program_twiddling to avoid code duplication. You can add an optional bias parameter to ref_program_twiddling and apply it conditionally. This would also simplify the validation logic in main.
Suggested change
| for i in range(B.shape[0]): | |
| for j in range(B.shape[1]): | |
| B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) | |
| scale_expanded = Scale.repeat_interleave(32, dim=1) | |
| if scale_expanded.shape[1] > B.shape[1]: | |
| scale_expanded = scale_expanded[:, :B.shape[1]] | |
| B *= (2**scale_expanded) |
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Actionable comments posted: 3
🧹 Nitpick comments (1)
examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py (1)
336-336: Consider conditional allocation of bias_shared to save memory when bias is not used.Currently,
bias_sharedis always allocated even whenwith_bias=False. This wastes shared memory unnecessarily.Consider allocating
bias_sharedconditionally:- bias_shared = T.alloc_shared(bias_shared_shape, out_dtype) + if with_bias: + bias_shared = T.alloc_shared(bias_shared_shape, out_dtype)Note: This would require adjusting the layout annotation logic below as well.
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examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py(12 hunks)
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examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py (7)
tilelang/language/allocate.py (1)
alloc_shared(20-35)tilelang/language/__init__.py (1)
annotate_layout(97-135)tilelang/language/copy.py (1)
copy(84-152)tilelang/language/fill.py (1)
clear(24-48)examples/dequantize_gemm/example_dequant_gemm_bf16_fp4_hopper.py (2)
ref_program_twiddling(352-370)matmul(48-349)examples/dequantize_gemm/utils.py (2)
torch_convert_bit_twiddling(4-50)torch_convert(53-93)tilelang/profiler/__init__.py (1)
assert_allclose(76-137)
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🔇 Additional comments (10)
examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py (10)
93-93: LGTM! Bias parameter added correctly to the matmul signature.The
with_biasparameter is appropriately added with a default value ofFalse, maintaining backward compatibility while enabling the new bias functionality.
144-144: LGTM! Bias tensor shapes correctly defined.The bias shape
(M, N)is correct for a GEMM operation, and the shared memory shape(block_M, block_N)appropriately matches the tiling dimensions.Also applies to: 148-148
318-318: LGTM! Bias tensor parameter correctly added to kernel signature.The bias tensor is appropriately added to the prim_func signature with the correct shape and dtype specification.
346-350: Layout annotation for bias_shared correctly conditioned on with_bias.The conditional annotation ensures that the bias buffer is only registered for swizzled layout when it's actually being used.
354-360: LGTM! Bias initialization logic is correctly implemented.The kernel properly:
- Copies the bias block from global to shared memory
- Initializes the accumulator (
C_local) with the bias values whenwith_bias=True- Clears the accumulator when
with_bias=FalseThis ensures the GEMM accumulation starts with the bias values when enabled.
376-376: LGTM! Reference function signature correctly updated to support optional bias.The addition of
bias=Nonemaintains backward compatibility while enabling bias support.
425-425: LGTM! Reference function correctly updated to support optional bias.The addition of
bias=Noneparameter maintains backward compatibility.
481-481: LGTM! Main function signature correctly updated.The
with_biasparameter is appropriately added with a default value ofFalse, maintaining backward compatibility.
534-542: LGTM! Test validation correctly handles all bias configurations.The code properly selects the appropriate reference implementation based on both
fast_dequantandwith_biasflags, ensuring correct validation for all four test configurations.
552-555: LGTM! Comprehensive test coverage for all bias and dequantization modes.The test cases appropriately cover all four combinations of
(fast_dequant, with_bias)configurations, ensuring thorough validation of the new bias functionality.
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| def ref_program_twiddling_with_bias(A, qB, Scale, bias): | ||
| """ | ||
| Compute A @ B^T where B is reconstructed from bit-twiddled 4-bit quantized data and per-block scales, returning bfloat16 results. | ||
| Converts the quantized matrix `qB` to floating-point via `torch_convert_bit_twiddling`, applies a per-element scale factor of 2^(Scale - 127) (where Scale indexes are grouped by 32 columns of B), computes the matrix product A · B^T in float, and casts the result to bfloat16. | ||
| Parameters: | ||
| A (torch.Tensor): Left operand with shape (M, K), used in floating precision. | ||
| qB (torch.Tensor): Quantized representation of B (packed 4-bit values) compatible with torch_convert_bit_twiddling. | ||
| Scale (torch.Tensor): Per-column-group scale values; Scale indices correspond to groups of 32 columns in B. | ||
| bias (torch.Tensor): Bias tensor with shape (M, N). | ||
| Returns: | ||
| torch.Tensor: Resulting matrix C with shape (M, N) in bfloat16. | ||
| """ | ||
| dtypeC = "bfloat16" | ||
| B = torch_convert_bit_twiddling(qB) | ||
| for i in range(B.shape[0]): | ||
| for j in range(B.shape[1]): | ||
| B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) | ||
| C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias | ||
| C = C.to(torch.__getattribute__(dtypeC)) | ||
| return C | ||
|
|
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Remove duplicated docstring and fix parameter order inconsistency.
The docstring for ref_program_twiddling_with_bias is duplicated and has inconsistent parameter/return order in the documentation.
Apply this diff to fix the documentation:
def ref_program_twiddling_with_bias(A, qB, Scale, bias):
"""
- Compute A @ B^T where B is reconstructed from bit-twiddled 4-bit quantized data and per-block scales, returning bfloat16 results.
+ Compute A @ B^T + bias where B is reconstructed from bit-twiddled 4-bit quantized data and per-block scales, returning bfloat16 results.
Converts the quantized matrix `qB` to floating-point via `torch_convert_bit_twiddling`, applies a per-element scale factor of 2^(Scale - 127) (where Scale indexes are grouped by 32 columns of B), computes the matrix product A · B^T in float, and casts the result to bfloat16.
Parameters:
A (torch.Tensor): Left operand with shape (M, K), used in floating precision.
qB (torch.Tensor): Quantized representation of B (packed 4-bit values) compatible with torch_convert_bit_twiddling.
Scale (torch.Tensor): Per-column-group scale values; Scale indices correspond to groups of 32 columns in B.
bias (torch.Tensor): Bias tensor with shape (M, N).
-
+
Returns:
torch.Tensor: Resulting matrix C with shape (M, N) in bfloat16.
"""📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| def ref_program_twiddling_with_bias(A, qB, Scale, bias): | |
| """ | |
| Compute A @ B^T where B is reconstructed from bit-twiddled 4-bit quantized data and per-block scales, returning bfloat16 results. | |
| Converts the quantized matrix `qB` to floating-point via `torch_convert_bit_twiddling`, applies a per-element scale factor of 2^(Scale - 127) (where Scale indexes are grouped by 32 columns of B), computes the matrix product A · B^T in float, and casts the result to bfloat16. | |
| Parameters: | |
| A (torch.Tensor): Left operand with shape (M, K), used in floating precision. | |
| qB (torch.Tensor): Quantized representation of B (packed 4-bit values) compatible with torch_convert_bit_twiddling. | |
| Scale (torch.Tensor): Per-column-group scale values; Scale indices correspond to groups of 32 columns in B. | |
| bias (torch.Tensor): Bias tensor with shape (M, N). | |
| Returns: | |
| torch.Tensor: Resulting matrix C with shape (M, N) in bfloat16. | |
| """ | |
| dtypeC = "bfloat16" | |
| B = torch_convert_bit_twiddling(qB) | |
| for i in range(B.shape[0]): | |
| for j in range(B.shape[1]): | |
| B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) | |
| C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias | |
| C = C.to(torch.__getattribute__(dtypeC)) | |
| return C | |
| def ref_program_twiddling_with_bias(A, qB, Scale, bias): | |
| """ | |
| Compute A @ B^T + bias where B is reconstructed from bit-twiddled 4-bit quantized data and per-block scales, returning bfloat16 results. | |
| Converts the quantized matrix `qB` to floating-point via `torch_convert_bit_twiddling`, applies a per-element scale factor of 2^(Scale - 127) (where Scale indexes are grouped by 32 columns of B), computes the matrix product A · B^T in float, and casts the result to bfloat16. | |
| Parameters: | |
| A (torch.Tensor): Left operand with shape (M, K), used in floating precision. | |
| qB (torch.Tensor): Quantized representation of B (packed 4-bit values) compatible with torch_convert_bit_twiddling. | |
| Scale (torch.Tensor): Per-column-group scale values; Scale indices correspond to groups of 32 columns in B. | |
| bias (torch.Tensor): Bias tensor with shape (M, N). | |
| Returns: | |
| torch.Tensor: Resulting matrix C with shape (M, N) in bfloat16. | |
| """ | |
| dtypeC = "bfloat16" | |
| B = torch_convert_bit_twiddling(qB) | |
| for i in range(B.shape[0]): | |
| for j in range(B.shape[1]): | |
| B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) | |
| C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias | |
| C = C.to(torch.__getattribute__(dtypeC)) | |
| return C |
🤖 Prompt for AI Agents
In examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py around
lines 400 to 423, remove the duplicated docstring block and update the remaining
docstring so the parameter order matches the function signature (A, qB, Scale,
bias) and the returns section matches the actual return type and shape; keep a
single concise description of behavior, ensure each Parameters entry describes
the correct tensor shape and role in that same order, and make the Returns entry
state "torch.Tensor: Resulting matrix C with shape (M, N) in bfloat16."
| for i in range(B.shape[0]): | ||
| for j in range(B.shape[1]): | ||
| B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) | ||
| C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias |
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💡 Verification agent
🧩 Analysis chain
Verify bias tensor dtype compatibility.
The bias is directly added to the float result before casting to bfloat16. Ensure the bias tensor is in the correct dtype to avoid potential precision issues.
Also applies to: 476-476
🏁 Script executed:
#!/bin/bash
# Check if there are any existing patterns for bias dtype handling in similar GEMM implementations
rg -n --type py "bias.*\.(to|float|bfloat)" -C 3Length of output: 4022
🏁 Script executed:
#!/bin/bash
# Check utils.py for any dtype conversion patterns
cat examples/dequantize_gemm/utils.py | head -100Length of output: 4866
🏁 Script executed:
#!/bin/bash
# Extract context around bias addition in the GEMM example (lines ~400-440)
sed -n '400,440p' examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.pyLength of output: 2297
🏁 Script executed:
#!/bin/bash
rg -n "bias" -C 5 examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.pyLength of output: 11826
🏁 Script executed:
#!/bin/bash
# Display main function to locate bias tensor creation and dtype
sed -n '440,580p' examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.pyLength of output: 4866
Cast bias to float32 before addition
To ensure the accumulator stays in float32 and avoid subtle precision/overflow issues when bias isn’t already torch.float32, explicitly convert it before adding to the matmul result:
• In ref_program_twiddling_with_bias (line 420):
- C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias
+ C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias.to(torch.float)• In ref_program_simple_with_bias (line 476):
- C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias
+ C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias.to(torch.float)This guarantees both operands of + are float32, before finally casting C to bfloat16.
📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias | |
| C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + bias.to(torch.float) |
examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py
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@coderabbitai rename bias into Bias in kernel argument |
|
Tip For best results, initiate chat on the files or code changes. 🧩 Analysis chain@LeiWang1999 I'll help you rename In the kernel function definition (around line 80-90): @T.prim_func
def main(
A: T.Buffer((M, K), in_dtype),
qB: T.Buffer((N, K // scale_size, scale_size // 8), "uint32"),
Scale: T.Buffer((N, K // scale_size), "float16"),
Bias: T.Buffer(bias_shape, out_dtype), # Changed from 'bias' to 'Bias'
C: T.Buffer((M, N), out_dtype),
):In the buffer allocation (around line 100-110): if with_bias:
Bias_shared = T.alloc_buffer(bias_shared_shape, out_dtype, scope="shared") # Changed from 'bias_shared' to 'Bias_shared'In the conditional layout annotation: if with_bias:
T.annotate_layout({Bias_shared: make_swizzle_layout(Bias_shared)}) # Changed from 'bias_shared' to 'Bias_shared'In the bias copying logic: if with_bias:
for i, j in T.grid(bias_shared_shape[0], bias_shared_shape[1]):
with T.block("Bias_shared"): # Changed block name from 'bias_shared' to 'Bias_shared'
vi, vj = T.axis.remap("SS", [i, j])
Bias_shared[vi, vj] = Bias[vi, vj] # Changed from 'bias_shared[vi, vj] = bias[vi, vj]'In the initialization of C_local: if with_bias:
C_local[vi, vj] = T.if_then_else(k == 0, Bias_shared[vi, vj], C_local[vi, vj]) # Changed from 'bias_shared' to 'Bias_shared'The key changes are:
This maintains consistency with the existing naming convention where other buffers like 🏁 Script executed: Length of output: 28253 🏁 Script executed: Length of output: 203 🏁 Script executed: Length of output: 7507 🏁 Script executed: Length of output: 64587 🏁 Script executed: Length of output: 125 🏁 Script executed: Length of output: 1078 @LeiWang1999 I'll help you rename the In the kernel function parameter declaration (Line 318): @T.prim_func
def main(
A: T.Tensor(A_shape, in_dtype),
B: T.Tensor(B_shape, storage_dtype),
Scale: T.Tensor(Scale_shape, storage_dtype),
Bias: T.Tensor(bias_shape, out_dtype), # Changed from 'bias' to 'Bias'
C: T.Tensor((M, N), out_dtype),
):In the bias copying operation (Line 355): if with_bias:
T.copy(Bias[by * block_M:(by + 1) * block_M, bx * block_N:(bx + 1) * block_N], # Changed from 'bias' to 'Bias'
bias_shared)
T.copy(bias_shared, C_local)The key changes are:
Note that the local shared buffer This change maintains consistency with the existing naming convention where input tensor parameters are capitalized (like |
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Actionable comments posted: 2
♻️ Duplicate comments (1)
examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py (1)
451-479: Fix docstring formatting; vectorize scaling; cast Bias to float.This mirrors prior feedback and keeps the reference path fast and readable.
Apply this diff:
@@ -def ref_program_simple_with_bias(A, qB, Scale, Bias): +def ref_program_simple_with_bias(A, qB, Scale, Bias): """ - Compute a BF16 matrix product A · B^T from a quantized B with simple (non-twiddling) dequantization. + Compute a BF16 matrix product A · B^T + Bias from a quantized B with simple (non-twiddling) dequantization. @@ - Parameters: - - Returns: - - A: 2D tensor representing the left operand (will be cast to float32 for the matmul). - - qB: Quantized representation of B accepted by `torch_convert`. - - Scale: 2D tensor of exponent offsets; Scale[i][g] is applied to columns j where g == j // 32. - - Bias: 2D tensor representing the Bias (will be cast to float32 for the matmul). - - - Returns: - - 2D bfloat16 tensor C containing the matrix product A · B^T. + Parameters: + A (torch.Tensor): Left operand (M, K); cast to float32 for matmul. + qB (torch.Tensor): Quantized B accepted by `torch_convert`. + Scale (torch.Tensor): Exponent offsets; Scale[i][g] applies to columns j where g == j // 32. + Bias (torch.Tensor): Bias (M, N); added in float32 before casting. + + Returns: + torch.Tensor: 2D bfloat16 tensor C = A · B^T + Bias. @@ - B = torch_convert(qB) - for i in range(B.shape[0]): - for j in range(B.shape[1]): - B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) - C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + Bias + B = torch_convert(qB) + # Vectorized scale application + scale_expanded = Scale.repeat_interleave(32, dim=1) + if scale_expanded.shape[1] > B.shape[1]: + scale_expanded = scale_expanded[:, :B.shape[1]] + B = B * (2**scale_expanded) + C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + Bias.to(torch.float) C = C.to(torch.__getattribute__(dtypeC)) return C
🧹 Nitpick comments (5)
examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py (5)
124-125: Docstring: add bias semantics and fix minor inaccuracies.
- Please document the expected Bias shape (currently full M×N) and dtype relationship to accum_dtype/out_dtype here.
- Minor nit: earlier in the same docstring, “in_dtype (e.g., 'fp4' in this file)” is misleading for this bf16 example. Suggest “e.g., 'bfloat16'”.
Apply this diff to tighten the docstring:
@@ - in_dtype (str): element type of A (e.g., "fp4" in this file). + in_dtype (str): element type of A (e.g., "bfloat16" in this file). @@ - with_bias (bool, optional): whether to add Bias to the output (default False). + with_bias (bool, optional): whether to add Bias to the output (default False). + Bias is expected as a full M×N tensor in out_dtype; it is incorporated into the + accumulator (accum_dtype) before GEMM accumulation for this block.
145-149: Bias is modeled as full M×N; consider supporting broadcasted bias to reduce bandwidth.Full-tensor M×N bias is flexible but expensive to move per tile. Many GEMM users rely on per-N (column) or per-M (row) broadcast bias. If feasible, add a mode to accept Bias of shape (N,) or (M,1) and broadcast during initialization. This can save shared memory and global bandwidth while matching common inference workloads.
If you want, I can sketch a variant that switches on a bias_mode enum ('none'|'row'|'col'|'full') and adjusts shapes/indexing accordingly.
318-321: Kernel signature always requires Bias even when with_bias=False.This is workable (you already set Bias defaulting in reference functions), but it does complicate the call surface and input generation. Consider emitting two kernels from matmul (one with Bias, one without) keyed on with_bias to keep signatures minimal.
Would any downstream examples import this kernel assuming a 3-input signature (A, B, Scale) today? If yes, you may want to gate the Bias parameter on with_bias to avoid breakage.
336-360: Initializer path copies Bias_shared (out_dtype) into C_local (accum_dtype). Confirm implicit dtype cast or upcast explicitly.T.copy(C_local→C_shared) elsewhere performs a downcast (accum_dtype→out_dtype). Here you rely on the inverse (out_dtype→accum_dtype). If T.copy guarantees type conversion in both directions, fine; if not, numeric corruption is possible.
Two options:
- Minimal: keep allocation as-is and explicitly upcast during init using a small parallel loop.
- Alternative: allocate Bias_shared in accum_dtype so the copy to C_local is type-match.
Apply either of the diffs below.
Option A (explicit upcast, safest without changing shared dtype):
@@ - if with_bias: - T.copy(Bias[by * block_M:(by + 1) * block_M, bx * block_N:(bx + 1) * block_N], - Bias_shared) - T.copy(Bias_shared, C_local) + if with_bias: + T.copy( + Bias[by * block_M:(by + 1) * block_M, bx * block_N:(bx + 1) * block_N], + Bias_shared + ) + for i, j in T.Parallel(block_M, block_N): + C_local[i, j] = Bias_shared[i, j].astype(accum_dtype) else: T.clear(C_local)Option B (allocate Bias_shared in accum_dtype to avoid per-element cast later):
- Bias_shared = T.alloc_shared(Bias_shared_shape, out_dtype) + Bias_shared = T.alloc_shared(Bias_shared_shape, accum_dtype) @@ - T.copy(Bias_shared, C_local) + T.copy(Bias_shared, C_local) # now type-matchedIf T.copy already upcasts safely, Option A is not strictly required—please confirm and keep as-is.
534-542: Validation branches are correct; can be simplified after unifying references.As-is, correctness is fine. If you adopt the “unified reference” suggestion, you can collapse these branches to a single call per path (twiddling/simple) and let the optional Bias argument do the right thing.
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examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py (6)
tilelang/language/__init__.py (1)
annotate_layout(97-135)tilelang/language/copy.py (1)
copy(84-152)tilelang/language/fill.py (1)
clear(24-48)examples/dequantize_gemm/example_dequant_gemm_bf16_fp4_hopper.py (4)
matmul(48-349)ref_program_simple(373-390)main(294-347)main(393-437)examples/dequantize_gemm/utils.py (2)
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assert_allclose(76-137)
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🔇 Additional comments (3)
examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py (3)
93-93: Plumbing the with_bias flag into the kernel builder is correct.The new parameter integrates cleanly with later uses in the prim_func and the driver (main). No functional concerns here.
345-350: Conditional layout annotation for Bias_shared is a nice touch.Keeps the main layout map lean unless bias is enabled. Looks good.
552-555: Nice: main exercises all four combinations.This improves demo coverage and catches regressions in both dequant paths with/without bias.
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| def ref_program_twiddling(A, qB, Scale): | ||
| def ref_program_twiddling(A, qB, Scale, Bias=None): |
There was a problem hiding this comment.
🛠️ Refactor suggestion
*Avoid duplicating reference functions; make Bias optional and drop the _with_bias variants.
You already added Bias=None to the bias-less references. Add a conditional add in-place and remove the duplicated *_with_bias functions. Then simplify the driver to always call the unified references.
Apply these diffs:
Unify ref_program_twiddling:
@@
-def ref_program_twiddling(A, qB, Scale, Bias=None):
+def ref_program_twiddling(A, qB, Scale, Bias=None):
@@
- C = torch.matmul(A.to(torch.float), B.T.to(torch.float))
+ C = torch.matmul(A.to(torch.float), B.T.to(torch.float))
+ if Bias is not None:
+ C = C + Bias.to(torch.float)
C = C.to(torch.__getattribute__(dtypeC))
return CUnify ref_program_simple:
@@
-def ref_program_simple(A, qB, Scale, Bias=None):
+def ref_program_simple(A, qB, Scale, Bias=None):
@@
- C = torch.matmul(A.to(torch.float), B.T.to(torch.float))
+ C = torch.matmul(A.to(torch.float), B.T.to(torch.float))
+ if Bias is not None:
+ C = C + Bias.to(torch.float)
C = C.to(torch.__getattribute__(dtypeC))
return CDrop the duplicated functions and simplify validation:
@@
- if fast_dequant:
- if with_bias:
- profiler.assert_allclose(ref_program_twiddling_with_bias, rtol=0.01, atol=0.01)
- else:
- profiler.assert_allclose(ref_program_twiddling, rtol=0.01, atol=0.01)
- else:
- if with_bias:
- profiler.assert_allclose(ref_program_simple_with_bias, rtol=0.01, atol=0.01)
- else:
- profiler.assert_allclose(ref_program_simple, rtol=0.01, atol=0.01)
+ if fast_dequant:
+ profiler.assert_allclose(ref_program_twiddling, rtol=0.01, atol=0.01)
+ else:
+ profiler.assert_allclose(ref_program_simple, rtol=0.01, atol=0.01)And remove the definitions of ref_program_twiddling_with_bias and ref_program_simple_with_bias entirely.
Also applies to: 425-425, 534-542
Comment on lines
+400
to
+414
| def ref_program_twiddling_with_bias(A, qB, Scale, Bias): | ||
| """ | ||
| Compute A @ B^T where B is reconstructed from bit-twiddled 4-bit quantized data and per-block scales, returning bfloat16 results. | ||
| Converts the quantized matrix `qB` to floating-point via `torch_convert_bit_twiddling`, applies a per-element scale factor of 2^(Scale - 127) (where Scale indexes are grouped by 32 columns of B), computes the matrix product A · B^T in float, and casts the result to bfloat16. | ||
| Parameters: | ||
| A (torch.Tensor): Left operand with shape (M, K), used in floating precision. | ||
| qB (torch.Tensor): Quantized representation of B (packed 4-bit values) compatible with torch_convert_bit_twiddling. | ||
| Scale (torch.Tensor): Per-column-group scale values; Scale indices correspond to groups of 32 columns in B. | ||
| Bias (torch.Tensor): Bias tensor with shape (M, N). | ||
| Returns: | ||
| torch.Tensor: Resulting matrix C with shape (M, N) in bfloat16. | ||
| """ |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Docstring: say “A @ B^T + Bias” and fix scale description; cast Bias to float.
- Summary line should include the bias addition.
- Current doc says 2^(Scale - 127) while the code multiplies by 2**Scale. Align the doc with the actual computation to avoid confusion.
- Cast Bias to float before addition to preserve fp32 accumulation.
Apply this diff:
@@
-def ref_program_twiddling_with_bias(A, qB, Scale, Bias):
+def ref_program_twiddling_with_bias(A, qB, Scale, Bias):
"""
- Compute A @ B^T where B is reconstructed from bit-twiddled 4-bit quantized data and per-block scales, returning bfloat16 results.
+ Compute A @ B^T + Bias where B is reconstructed from bit-twiddled 4-bit quantized data and per-block scales, returning bfloat16 results.
@@
- Converts the quantized matrix `qB` to floating-point via `torch_convert_bit_twiddling`, applies a per-element scale factor of 2^(Scale - 127) (where Scale indexes are grouped by 32 columns of B), computes the matrix product A · B^T in float, and casts the result to bfloat16.
+ Converts the quantized matrix `qB` to floating-point via `torch_convert_bit_twiddling`, applies a per-element scale factor of 2**Scale (where Scale indexes are grouped by 32 columns of B), computes the matrix product A · B^T in float, adds Bias, and casts the result to bfloat16.
@@
- C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + Bias
+ C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + Bias.to(torch.float)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| def ref_program_twiddling_with_bias(A, qB, Scale, Bias): | |
| """ | |
| Compute A @ B^T where B is reconstructed from bit-twiddled 4-bit quantized data and per-block scales, returning bfloat16 results. | |
| Converts the quantized matrix `qB` to floating-point via `torch_convert_bit_twiddling`, applies a per-element scale factor of 2^(Scale - 127) (where Scale indexes are grouped by 32 columns of B), computes the matrix product A · B^T in float, and casts the result to bfloat16. | |
| Parameters: | |
| A (torch.Tensor): Left operand with shape (M, K), used in floating precision. | |
| qB (torch.Tensor): Quantized representation of B (packed 4-bit values) compatible with torch_convert_bit_twiddling. | |
| Scale (torch.Tensor): Per-column-group scale values; Scale indices correspond to groups of 32 columns in B. | |
| Bias (torch.Tensor): Bias tensor with shape (M, N). | |
| Returns: | |
| torch.Tensor: Resulting matrix C with shape (M, N) in bfloat16. | |
| """ | |
| def ref_program_twiddling_with_bias(A, qB, Scale, Bias): | |
| """ | |
| Compute A @ B^T + Bias where B is reconstructed from bit-twiddled 4-bit quantized data and per-block scales, returning bfloat16 results. | |
| Converts the quantized matrix `qB` to floating-point via `torch_convert_bit_twiddling`, applies a per-element | |
| scale factor of 2**Scale (where Scale indexes are grouped by 32 columns of B), computes the matrix product | |
| A · B^T in float, adds Bias, and casts the result to bfloat16. | |
| Parameters: | |
| A (torch.Tensor): Left operand with shape (M, K), used in floating precision. | |
| qB (torch.Tensor): Quantized representation of B (packed 4-bit values) compatible with torch_convert_bit_twiddling. | |
| Scale (torch.Tensor): Per-column-group scale values; Scale indexes correspond to groups of 32 columns in B. | |
| Bias (torch.Tensor): Bias tensor with shape (M, N). | |
| Returns: | |
| torch.Tensor: Resulting matrix C with shape (M, N) in bfloat16. | |
| """ | |
| B = torch_convert_bit_twiddling(qB) | |
| scale_expanded = Scale.repeat_interleave(32, dim=1) | |
| if scale_expanded.shape[1] > B.shape[1]: | |
| scale_expanded = scale_expanded[:, :B.shape[1]] | |
| B *= (2**scale_expanded) | |
| C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + Bias.to(torch.float) | |
| return C.to(torch.bfloat16) |
🤖 Prompt for AI Agents
In examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py around
lines 400 to 414, update the docstring and code: change the one-line summary to
state that the function computes "A @ B^T + Bias"; correct the scale description
to reflect that the code multiplies by 2**Scale (not 2^(Scale - 127)) and note
that Scale indexes correspond to groups of 32 columns of B; and ensure Bias is
cast to float (fp32) before being added so accumulation stays in float prior to
casting the final result to bfloat16.
LeiWang1999
approved these changes
Aug 24, 2025
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Oct 24, 2025
* [Index] Relocate Int64 Auto Promoter to ConfigBitWidth Pass, removing it from FlattenBuffer (#714) * Update submodule 'tvm' to commit e11521e6936a827efa334588d29571fbb4620107 * Refactor inject_pipeline.cc to enhance pipeline body rewriting and condition handling - Introduced a new function to replace IfThenElse nodes with their then_case while preserving attributes. - Streamlined the PipelineBodyRewriter to improve buffer access rewriting and async state management. - Enhanced the handling of pipeline loop conditions and added support for predicate conditions in the pipeline body. - Removed obsolete code and improved overall code clarity and maintainability. * lint fix * Refactor return statements in inject_pipeline.cc to remove unnecessary std::move calls - Updated return statements in multiple methods to return objects directly instead of using std::move, improving code clarity and potentially avoiding unnecessary moves. - Ensured consistent handling of BufferStore and BufferLoad nodes during pipeline transformations. * test fix * Enhance global read detection in pipeline planning - 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Enhanced buffer index handling to address precision issues by removing redundant operations. - Streamlined the logic for determining buffer overlaps, ensuring more accurate conflict detection. - Updated related documentation to reflect changes in buffer management practices. * Remove obsolete test script for AMD example, streamlining the examples directory. * Remove unused dtype_size variable in AMD example script to streamline code. * Add input configuration file and update AMD example script for enhanced flexibility - Introduced a new input.txt file for configurable parameters. - Modified the example_amd_flash_attn_fwd.py script to allow for a wider range of configurations, including additional options for num_stages, enable_rasterization, and k_pack. - Streamlined the main function for better clarity and organization. - Added a new test script to facilitate running the example with specified parameters. * Remove input configuration file and obsolete test script; enhance AMD example with swizzle layout annotations - Deleted input.txt and test.sh files as they are no longer needed. - Updated example_amd_flash_attn_fwd.py to include swizzle layout annotations for shared memory, improving bank conflict avoidance. - Reintroduced swizzle usage in the kernel for better performance. * Refactor AMD example script for FlashAttention-2 - Updated function names for clarity, changing `get_v2_configs` to `get_configs` and `fast_flashattn_v2` to `fast_flashattn`. - Streamlined the main function by renaming `main_v2` to `main` and adjusting the corresponding calls. - Removed outdated comments and improved code organization for better readability. * Refactor formatting in AMD FlashAttention example script - Improved code readability by adjusting line breaks and indentation in the `fast_flashattn` function. - Streamlined the `main` function parameter formatting for consistency. - Removed unnecessary blank lines to enhance overall code organization. * Update example_amd_flash_attn_fwd.py * Update AMD FlashAttention example and TVM submodule - Added a new example script `example_amd_flash_attn_fwd_k_block.py` for FlashAttention with K-blocking support. - Enhanced `example_amd_flash_attn_fwd.py` by expanding configuration options for block sizes and threads. - Updated the TVM submodule to the latest commit for improved functionality. - Introduced a new test script `test.sh` to facilitate running the new example with specified parameters. * Add CI workflow for automated format checking and testing - Introduced a new GitHub Actions workflow in `amd_ci.yml` to automate format checks and testing for pull requests. - The workflow includes steps for setting up a Python environment, running format checks, and executing tests. - Removed obsolete example script `example_amd_flash_attn_fwd_k_block.py` and test script `test.sh` to streamline the examples directory. * Rename CI workflow from "CI" to "AMD CI" for clarity and specificity. * Update AMD CI workflow to include copying PyTorch, TorchVision, and Torchaudio packages to the virtual environment for improved dependency management. * Update AMD CI workflow to install pytest directly instead of using requirements-test.txt * Update AMD CI workflow to remove 'flash-attn' from requirements and install dependencies from requirements-test.txt * Refactor AMD CI workflow to enhance clarity in removing 'flash-attn' from requirements-test.txt before installation * Remove Torchaudio package copying from AMD CI workflow to streamline dependency management. * Refactor AMD CI workflow to remove the format-check job and streamline the build-test process by directly copying PyTorch and TorchVision packages to the virtual environment. * Add installation of ROCm in AMD CI workflow - Included a step to execute the `install_rocm.sh` script for improved setup. - Removed unnecessary blank line for better readability in the workflow script. * Remove installation step for ROCm in AMD CI workflow to simplify the setup process. * Update AMD CI workflow to run specific test file with verbose output instead of all tests. * Add new tilelang built-in operations for AMD architecture - Introduced `tvm_mfma`, `tvm_mfma_store`, `tvm_rdna_wmma`, and `tvm_rdna_wmma_store` built-in operations to enhance support for matrix multiplication and storage in tilelang. - Each operation is configured with the appropriate number of inputs and marked as opaque in terms of call effects. * Enhance autotuner configurations and GEMM operations in AMD example - Updated block sizes and num_split_q parameters in `get_configs` for improved autotuning. - Modified `T.gemm` calls in `fast_flashattn` to utilize `GemmWarpPolicy.FullRow`, optimizing performance for matrix multiplications. * Update autotuner configurations in AMD example for enhanced performance - Refined block sizes, thread counts, and added new parameters in `get_configs` to optimize autotuning. - Adjusted `fast_flashattn` function to incorporate new parameters for panel size and coalesced widths, improving memory access patterns. * Enhance autotuner configurations and memory handling in AMD example - Expanded block sizes and thread counts in `get_configs` for improved autotuning capabilities. - Updated `fast_flashattn` to utilize a new shared memory allocation strategy, optimizing memory access patterns during GEMM operations. * Refine autotuner configurations and memory usage in AMD example - Reduced block sizes and adjusted thread counts in `get_configs` for optimized autotuning. - Updated `fast_flashattn` to utilize register fragments for accumulation, minimizing LDS usage and enhancing performance during GEMM operations. * Update autotuner configurations in AMD example for enhanced performance - Expanded block sizes and thread counts in `get_configs` to improve autotuning capabilities. - Adjusted `num_split_q` and `v_coalesced_width` parameters for better optimization during GEMM operations. * Enhance autotuner configurations and GEMM operations in AMD example - Expanded thread counts in `get_configs` to include higher values for improved autotuning. - Updated `fast_flashattn` to adjust accumulation logic and ensure proper handling of causal conditions, optimizing performance during matrix multiplications. * Update AMD CI workflow and remove obsolete test script - Modified the CI workflow to run on multiple environments: self-hosted, amd, and gpu. - Deleted the outdated `test.sh` script from the examples directory, streamlining the project structure. * Remove TVM subproject from 3rdparty directory * Refactor configuration generation and accumulation logic in AMD example - Reformatted the `get_configs` function for improved readability by aligning parameters. - Adjusted the `fast_flashattn` function to enhance clarity in the conditional logic for accumulation, ensuring better handling of causal conditions. * Enhance AMD CI workflow with additional logging and setup steps - Added echo statements to provide feedback during the CI process, indicating when the environment is running on an AMD GPU, copying necessary packages, and installing requirements. - Improved clarity in the workflow by explicitly stating when the project is being installed and when tests are being executed. * Comment out package copying in AMD CI workflow to prevent potential issues during environment setup * Update AMD CI workflow to install nightly versions of PyTorch and remove obsolete package copying steps * Enhance BuildTileLangHIP function by adding whitespace for improved readability * Refactor kTVMGridConstant definition for clarity and remove unnecessary comment * Update TVM subproject to latest commit a64a5926a6e59f5417ef2501f9d88b467337cf6a * lint fix * Update AMD CI workflow to use requirements-rocm.txt for dependency installation * fix ci * Remove dependency on format-check from AMD CI workflow * fix ci * fix ci * fix ci * Remove format-check job from AMD CI workflow * Add torch to requirements-rocm.txt and remove explicit pip install commands from AMD CI workflow * Add dependency on format-check job in AMD CI workflow * Add format-check job to AMD CI workflow * Update format-check job in AMD CI workflow to run on self-hosted environment * Enhance format-check job in AMD CI workflow with improved Python environment setup and automatic commit of lint changes * Update amd_ci.yml --------- Co-authored-by: xinxyxiao <xinyxiao@amd.com> Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> * [Carver][Bugfix] Correct score function for warp tile selection in tensorcore policy (#724) * [Carver][Bugfix] Correct score function for warp tile selection in tensorcore policy * [Typo] Correct architecture selection for CUDA and CDNA * [Refactor] Refactor CUDA code generation to simplify eviction policy handling (#721) * Update submodule 'tvm' to commit e11521e6936a827efa334588d29571fbb4620107 * Refactor CUDA code generation to simplify eviction policy handling - Updated `VisitExpr_` methods in `codegen_cuda.cc` to use default eviction policy for `tma_load`, `tma_load_im2col`, and `tma_store` functions, reducing complexity. - Removed conditional assembly code for `EVICT_NORMAL` in `copy_sm90.h`, streamlining the assembly calls for tensor memory operations. * lint fix * [Language] Introduce `StridedTensor` to support non contigious torch inputs (#722) * Update submodule 'tvm' to commit e11521e6936a827efa334588d29571fbb4620107 * Support strided tensors * Refactor target attribute helper functions for improved clarity * No code changes made in proxy.py and setup.py * lint fix * lint fix via gemini * lint fix * test fix * test fix * lint fix * Update wrapper.py * test fix * Enhance test for InjectSoftwarePipeline by adding LowerOpaqueBlock transformation and updating expected function signature to use match_buffer for better clarity. * lint fix --------- Co-authored-by: Chenggang Zhao <chenggangz@deepseek.com> * [Enhancement][Bugfix] Fix bug in warp specialized pass and add gemm_sr fallback support for Hopper (#712) * bug fix and support gemm_sr fallback for hopper * Update gemm.cc --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> * 📝 Add docstrings to `fix` (#726) Docstrings generation was requested by @LeiWang1999. * https://github.com/tile-ai/tilelang/pull/712#issuecomment-3190680851 The following files were modified: * `src/op/gemm.cc` * `src/tl_templates/cuda/gemm_sm90.h` * `src/transform/warp_specialized_rewriter.cc` Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> * [CI] Fix AMD CI (#729) * [Enhancement] Refactor buffer index handling for improved precision and clarity (#668) - Enhanced buffer index handling to address precision issues by removing redundant operations. - Streamlined the logic for determining buffer overlaps, ensuring more accurate conflict detection. - Updated related documentation to reflect changes in buffer management practices. * Remove obsolete test script for AMD example, streamlining the examples directory. * Remove unused dtype_size variable in AMD example script to streamline code. * Add input configuration file and update AMD example script for enhanced flexibility - Introduced a new input.txt file for configurable parameters. - Modified the example_amd_flash_attn_fwd.py script to allow for a wider range of configurations, including additional options for num_stages, enable_rasterization, and k_pack. - Streamlined the main function for better clarity and organization. - Added a new test script to facilitate running the example with specified parameters. * Remove input configuration file and obsolete test script; enhance AMD example with swizzle layout annotations - Deleted input.txt and test.sh files as they are no longer needed. - Updated example_amd_flash_attn_fwd.py to include swizzle layout annotations for shared memory, improving bank conflict avoidance. - Reintroduced swizzle usage in the kernel for better performance. * Refactor AMD example script for FlashAttention-2 - Updated function names for clarity, changing `get_v2_configs` to `get_configs` and `fast_flashattn_v2` to `fast_flashattn`. - Streamlined the main function by renaming `main_v2` to `main` and adjusting the corresponding calls. - Removed outdated comments and improved code organization for better readability. * Refactor formatting in AMD FlashAttention example script - Improved code readability by adjusting line breaks and indentation in the `fast_flashattn` function. - Streamlined the `main` function parameter formatting for consistency. - Removed unnecessary blank lines to enhance overall code organization. * Update example_amd_flash_attn_fwd.py * Enhance AMD example script and update CI workflows - Improved the `example_amd_flash_attn_fwd.py` script for better clarity and organization. - Added new CI workflows for AMD and documentation publishing. - Updated various requirements files to include necessary dependencies. - Introduced new test cases and examples for better coverage and functionality. - Refactored existing code for improved readability and maintainability. * Remove redundant tool cache cleanup step in AMD CI workflow * Remove `torch` dependency from `requirements-rocm.txt` to streamline requirements. --------- Co-authored-by: xinxyxiao <xinyxiao@amd.com> Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> * [Feature] Low-bit twiddling dequantization and FP4 GEMM (#725) * [Dequant] Add bit-twiddling dequantize cuda for fp4-->bf16 * [Dequant] Add extern call and serial dequantization * [Dequant] Parallel Dequant wait for fence debug. * [Scale] Add scale matrix to mxfp4 gemm * [Remove] Remove fence-buggy example and some generated source cuda code * [MXFP4] Update initial version of MXFP4 GEMM * [Scale] Add scale to latest mxfp4 gemm * [Lint] * [BugFix] Load Scale, disabe TMA to recover performance * [Lint] * [Lint] * [Scale] Use L2 to hold Scale and enable TMA will slightly boost performance * [Lint] * Update example_dequant_gemm_bf16_fp4_hopper_serial.py * Remove deprecated dequantization examples for BF16 and MXFP4 in the dequantize_gemm directory. * Refactor dequantization examples for improved readability and consistency. Adjusted formatting in matmul function and added spacing for clarity. Updated function signatures and comments for better understanding. * Refactor index_to_coordinates usage in bitnet example and update dequantization example configurations. Removed the custom index_to_coordinates function and replaced it with the built-in version. Adjusted block_K parameter in dequantization example for consistency. * lint fix * ci fix * Remove non-existent example * [BugFix] Add smem swizzle to recover performance of TMA * [BugFix] Enough reg for producer when threads=512 --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> * 📝 Add docstrings to `mxfp4` (#732) * 📝 Add docstrings to `mxfp4` Docstrings generation was requested by @LeiWang1999. * https://github.com/tile-ai/tilelang/pull/725#issuecomment-3191656561 The following files were modified: * `examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_prefill.py` * `examples/dequantize_gemm/example_dequant_gemm_bf16_fp4_hopper.py` * `examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py` * `examples/dequantize_gemm/utils.py` * `examples/gemm/example_gemm_autotune.py` * `tilelang/intrinsics/utils.py` * `tilelang/language/__init__.py` * `tilelang/language/utils.py` * `tilelang/quantize/mxfp.py` * `tilelang/quantize/quantization.py` * [Lint] More accurate docstring * [Lint] --------- Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: tzj-fxz <tzjfxz@gmail.com> * [Refactor] Refactor env into a more flexible version (#740) * Fix environment variable name for compilation print setting in `env.py` * Remove deprecated test file for warp specialized pass configuration and refactor environment variable access in `env.py` to utilize a centralized `EnvVar` class for better management and clarity. * lint fix * Refactor cache check to use `env.is_cache_enabled()` for consistency in `tuner.py` * [Enhancement] Add stride index validation in CythonKernelWrapper (#743) * Introduced an assertion to ensure that the stride index is within the valid range of tensor dimensions in `cython_wrapper.pyx`. * This change prevents potential out-of-bounds errors when accessing tensor dimensions, enhancing the robustness of the code. * [Bugfix]:Fix atomic add auto vectorize memory access out of bound error (#742) * [Bugfix]:Fix atomic add auto vectorize memory access out of bound error * Update atomicadd_vectorize.cc * format * 📝 Add docstrings to PR #744 (#745) * 📝 Add docstrings to `main` Docstrings generation was requested by @LeiWang1999. * https://github.com/tile-ai/tilelang/pull/742#issuecomment-3205103559 The following files were modified: * `src/transform/atomicadd_vectorize.cc` * lint fix --------- Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> * [Refactor] Refactor barrier management (#744) * Introduce Barrier * Enhance CUDA kernel with new barrier management and post-processing support - Added a new CUDA kernel implementation in `example_mla_decode.py` for improved performance with shared memory barriers. - Refactored barrier handling in `codegen_cuda.cc` and `codegen_hip.cc` to utilize a more flexible mbarrier structure. - Updated intrinsic definitions from `ptx_stmatirx` to `ptx_stmatrix` across multiple files for consistency. - Introduced additional print statements for debugging in the lowering phase of the TileLang engine. - Enhanced the overall structure and readability of the codebase. * Remove unused barrier handling code in CUDA and HIP code generators to streamline the implementation. This change enhances code clarity and reduces complexity in the barrier management logic. * Enhance barrier management in TileLang - Introduced a new intrinsic `allocate_barrier` for dynamic barrier allocation in the TileLang framework. - Updated CUDA code generation to support the new barrier structure, allowing for improved synchronization in shared memory. - Refactored existing barrier handling logic to accommodate the new intrinsic and streamline code. - Added print statements for debugging purposes in various examples and the lowering phase of the TileLang engine. - Removed deprecated memory scope handling code to enhance clarity and maintainability. * lint fix * lint fix * Remove `allocate_barrier` intrinsic and related code from TileLang to streamline barrier management. This includes updates to CUDA code generation and the removal of associated Python wrappers, enhancing code clarity and maintainability. * Refactor logging in JITKernel to improve kernel compilation tracking - Removed unused import of `torch.backends` in the example file. - Introduced logging for kernel compilation in `JITKernel`, replacing print statements with structured logging for better traceability and debugging. - Added an assertion to ensure the presence of the `global_symbol` attribute in the kernel function. * Refactor dequantization tests and update barrier function - Removed the test for `example_dequant_gemm_bf16_fp4_hopper_serial` to streamline the testing suite. - Updated the `mbarrier_cp_async_arrive` function to support both pointer and non-pointer types, enhancing flexibility in barrier management. * Update CI configuration to increase pytest parallelism from 4 to 8 threads for improved test execution speed. * Fix typos in rasterization parameters and update import path for cached module - Corrected the spelling of `enable_rasteration` to `enable_rasterization` in the matmul function and its usage. - Updated the import statement for the `cached` module to reflect the new path in the cache submodule. - Added `StridedTensor` import in the language module for enhanced tensor functionality. * Update ci.yml * [Refactor] Merge bulk copy into copy and improve layout inference for bulk copy (#746) * [Refactor] Merge bulk copy into copy and refactor layout inference for bulk copy * Deleted the `bulk_copy` operator implementation and its header file as it is no longer needed. * Introduced a new function `cuTensorMapType()` to return the data type for CUDA tensor mapping. * Updated related files to reflect these changes, ensuring that the codebase remains clean and maintainable. * lint fix * Fix typos in intrinsic names and remove unused print statement in block_sparse_attn_tilelang.py. Updated references from `ptx_ldmatirx` to `ptx_ldmatrix` across multiple files for consistency. * remove bulk copy * Refactor copy and atomic add operations to support TMA lower configuration - Updated `GetCopyInst` to accept a `disable_tma_lower` parameter, allowing for conditional usage of TMA in bulk load/store operations. - Modified `Lower` method in `Copy` to incorporate the new TMA configuration. - Refactored `AtomicAdd::Lower` to streamline layout inference and vectorization logic. - Removed unused `disable_tma_lower` field from `LowerArgs` structure for clarity. - Enhanced atomic add vectorization by replacing the buggy implementation with a more robust loop vectorization approach. * Enhance TMA bulk copy logic in `LowerBulkCopy` method - Added a condition to set `desc.swizzle` to `CU_TENSOR_MAP_SWIZZLE_NONE` when `shared_layout` matches `linear_layout`, improving clarity in layout handling. - Updated warning log to provide more detailed information about fallback scenarios, including source and destination buffer names and shapes, enhancing debugging capabilities. * lint fix * Remove fallback logging for non-swizzled global layout in `LowerBulkCopy` method to streamline the bulk copy logic. This change enhances code clarity by eliminating unnecessary warning messages related to inner box dimensions. * Enhance reshape kernel compilation in `run_reshape` and `run_reshape_smem_1d_2_2d` functions - Updated the `tl.compile` method to include `pass_configs` that disable TMA lower and warp specialization, addressing shared memory layout transformation limitations. - Added TODO comments to indicate the need for further improvements in shared memory handling. * Update `native_sparse_attention` function to include TMA configuration options - Added `pass_configs` to the JIT decorator to disable TMA lower and warp specialization, addressing potential issues with shared memory layout transformations. - Updated comments to clarify modifications in tensor shapes for inference, specifically setting `q` sequence length to 1. * Refactor JIT decorator formatting in `native_sparse_attention` function - Improved readability by reformatting the JIT decorator parameters for `native_sparse_attention`, ensuring consistent style across the codebase. - No functional changes were made; this update focuses on code clarity and maintainability. * Enhance thread management and logging in TileLang compilation - Added a method to check if printing is enabled during compilation, improving control over logging behavior. - Updated the JIT kernel class to utilize the new method for logging compilation status, ensuring consistent and clear output. - Added comments to clarify the purpose of changes and improve code readability. * Add warp specialization scope and refactor register management in TileLang - Introduced a new constant `kWarpSpecializationScope` in `builtin.h` for better attribute management. - Removed the `SetMaxNRegCollector` class and its related logic from `warp_specialized_rewriter.cc`, streamlining the warp specialization process. - Added functions `annotate_producer_reg_dealloc` and `annotate_consumer_reg_alloc` in `builtin.py` to facilitate register management. - Implemented `AnnotateWarpGroupRegAlloc` in `__init__.py` to inject register allocation calls into warp-specialized functions, enhancing the overall register handling in the compilation process. * Refactor test for InjectSetMaxNReg pass in TileLang - Improved readability by restructuring conditional checks and assertions in the test cases. - Enhanced clarity in the collection of `set_max_nreg` calls by simplifying the logic. - Ensured consistent formatting and spacing throughout the test functions for better maintainability. * Enhance bulk copy and store checks in `Copy` class - Updated scope validation for source and destination tensors in `CheckBulkLoad` and `CheckBulkStore` methods to include both `shared.dyn` and `shared` as valid options. - Modified `CheckLDSMCopy` and `CheckSTSMCopy` methods to accommodate the new scope validation, ensuring compatibility with shared memory configurations. - Improved logging in `LowerBulkCopy` to provide clearer warnings regarding unsupported swizzle layouts, including source and destination names for better debugging. * lint fix * [Refactor] Merge ThreadPartialSync and ThreadStorageSync (#741) * Remove `thread_partial_sync.cc` and refactor `thread_storage_sync.cc` to streamline synchronization handling. Introduce `thread_sync_types.h` for thread-bound key definitions and reserved named barriers. Update related logic in `ThreadSyncInserter` and `TileLangThreadSync` for improved clarity and efficiency. * Remove `sync_thread_partial` references and related documentation from the codebase. Update CUDA and HIP code generation files to eliminate calls to the removed function. Refactor `__sync_thread_partial` to `sync_thread_partial` in CUDA common header for consistency. * Remove unused import of `bulk_copy.h` in `codegen_hip.cc` to enhance code clarity and maintainability. * Add import of `bulk_copy.h` in `codegen_hip.cc` to support new functionality. * typo fix * Update data type in reduce_sum tests from float16 to float32 for consistency and clarity. Remove redundant dtype tests and streamline run functions. Enhance reshape kernel compilation with pass configurations to address shared memory layout issues. * lint fix * test fix * Enhance CI configuration by adding verbose output to pip install command for better visibility during installation. * use ninja instead of make * Add CMake configuration step for Ninja build system in setup.py * Update pyproject.toml to include additional build dependencies: build, torch, tox, auditwheel, patchelf, and ninja. * Enhance CI configuration by adding verbose output to pytest commands for improved test visibility. * Update pyproject.toml to add Cython as a build dependency. Enhance thread storage synchronization in thread_storage_sync.cc by introducing new thread variable handling and improving index disjointness checks. * Update data type in cumulative sum tests from float16 to float32 for consistency. Modify run_cumsum function to utilize the updated dtype and enhance result validation with assertions. Adjust test cases accordingly. * Refactor storage access handling by introducing buffer data mapping in TileLangStorageAccessVisitor. Enhance access entry structure to include pointer access flag. Update thread storage synchronization to accommodate new buffer data mappings. Adjust quickstart example to print kernel source for debugging purposes. * Refactor linear index conversion in TileLangStorageAccessVisitor to utilize the analyzer for simplification. Update buffer index calculations to ensure consistent simplification of range expressions. * bugfix * Refactor buffer index calculation in TileLangStorageAccessVisitor to simplify access handling. Removed unused buffer mapping logic, ensuring consistent buffer index generation with a default ramp. * Refactor TileLangStorageAccessVisitor to replace buffer indices with buffer ranges for improved pointer access handling. Update AccessEntry structure to include buffer_ranges and adjust thread storage synchronization logic to account for pointer access conflicts. * Refactor thread storage synchronization to replace 'shared.dyn' with 'shared' for consistency in memory allocation. Update related test cases to reflect this change and ensure proper functionality. * [Enhancement] Optimize loop body handling in IR (#749) - Updated the loop body construction in `ir.cc` to conditionally include an output statement based on the analyzable condition of the `waves` variable. - This change enhances performance by avoiding unnecessary statement wrapping when the condition is met, improving the efficiency of loop execution. Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> * [MXFP4] Fix bugs and optimize exponential operation (#750) * [MXFP4] Fix bugs - Optimize exp2 with shift operation to boost performance - Fix bug of simple dequantization function call - Fix bug of scaling factor with bias * [Lint] --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> * [Enhancement] Add DispatchInstruction specialization for fp8 types in gemm_sm90.h (#751) - Introduced specialized DispatchInstruction templates for fp8_e4_t and fp8_e5_t types, enhancing support for new data formats in CUDA GEMM operations. - Each specialization defines the corresponding MMA and MMA_Group types, optimizing performance for specific configurations. * [Enhancement] Add shape checking for reduce options (#748) * Add shape checking for reduce options * lint fix * Handle special case reducing into shape-1 tensor Allow reducing [X, d, Y] into [X, Y] or [X, 1, Y] --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> * [Bugfix] Add missing FP8 header include (#752) * [Enhancement] Add DispatchInstruction specialization for fp8 types in gemm_sm90.h - Introduced specialized DispatchInstruction templates for fp8_e4_t and fp8_e5_t types, enhancing support for new data formats in CUDA GEMM operations. - Each specialization defines the corresponding MMA and MMA_Group types, optimizing performance for specific configurations. Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> * [Enhancement] Include cuda_fp8.h in gemm_sm90.h - Added the inclusion of the "cuda_fp8.h" header file to support new data formats in CUDA GEMM operations, enhancing compatibility with recent updates for fp8 types. Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> * lint fix * [Refactor] Remove unused tl_shuffle_elect and related functions from common.h - Deleted the `tl_shuffle_elect` function and its associated comments to streamline the codebase. - Added inclusion of "intrin.h" for improved intrinsic support in CUDA operations. - Cleaned up the file by removing unnecessary template parameters and functions, enhancing clarity and maintainability. * lint fix * [Refactor] Update header inclusions in common.h and gemm_sm90.h - Removed the inclusion of "intrin.h" from common.h to streamline dependencies. - Added "intrin.h" inclusion in gemm_sm90.h to ensure intrinsic support for CUDA operations, enhancing functionality and maintainability. * bug fix * [MXFP4] Add bias to MXFP4 GEMM kernel (#753) * [MXFP4] Add bias to gemm kernel * [Lint] * [Lint] Rename "bias" to "Bias" * [Bugfix][WS] Consider loop min extent when computing phase id (#754) * Update test parameters and remove debug print statement - Adjusted test cases in `test_tilelang_dynamic_symbolic_bench.py` to use smaller matrix sizes (1024x1024) for improved performance and quicker execution. - Removed a debug print statement from `phase.py` to clean up the code and enhance clarity. * Refactor loop stack management in warp_specialized_rewriter - Introduced a new `LoopInfo` struct to encapsulate loop variable details, including `loop_var`, `extent`, and `min`, enhancing clarity and maintainability. - Updated the `loop_stack_` to utilize `LoopInfo` instead of a pair, improving type safety and readability. - Adjusted linear index calculations to account for the new structure, ensuring correct behavior in loop transformations. * [Typo] Remove `disable_cache` in some tests (#755) * Update test parameters and remove debug print statement - Adjusted test cases in `test_tilelang_dynamic_symbolic_bench.py` to use smaller matrix sizes (1024x1024) for improved performance and quicker execution. - Removed a debug print statement from `phase.py` to clean up the code and enhance clarity. * Refactor loop stack management in warp_specialized_rewriter - Introduced a new `LoopInfo` struct to encapsulate loop variable details, including `loop_var`, `extent`, and `min`, enhancing clarity and maintainability. - Updated the `loop_stack_` to utilize `LoopInfo` instead of a pair, improving type safety and readability. - Adjusted linear index calculations to account for the new structure, ensuring correct behavior in loop transformations. * Remove unused `torch.backends` import and `tilelang.disable_cache()` calls from multiple test files to enhance code clarity and maintainability. * [README] Update GDN README for clarity and add acknowledgements (#758) - Improved formatting and clarity of the GDN kernel implementation description. - Updated requirement section to list dependencies in a clearer format. - Added an acknowledgements section to credit the developers and the Xiaomi LLM-Core Team for their contributions. * cutlass v4.2.0 supporting cuda 13 (#760) * [Feature] Add 1D TMA support (#761) * [Feature] Add 1D TMA support - Check the contiguous conditions of 1D TMA copy - Add new interface and params order of `tma_load` and `tma_store` call - Add 1D `tma_store` interface in sm90 template - Add elementwise kernel for 1D TMA example * [Lint] * [BugFix] Add conditions for 1D TMA copy on non-swizzle shared tensors * [Lint] * [BugFix] 1D TMA load * [README] Update GDN README for clarity and add acknowledgements (#758) - Improved formatting and clarity of the GDN kernel implementation description. - Updated requirement section to list dependencies in a clearer format. - Added an acknowledgements section to credit the developers and the Xiaomi LLM-Core Team for their contributions. * cutlass v4.2.0 supporting cuda 13 (#760) * [Lint] * [Lint] * [MXFP4] Add test for bf16&mxfp4 gemm * [BugFix] * [Lint] --------- Co-authored-by: Yu Cheng <54519279+chengyupku@users.noreply.github.com> Co-authored-by: Johnny <johnnync13@gmail.com> * [Example] Add vertical slash sparse attention pattern (#762) * upd sparse attn * lint * rename * update test file * update benchmark * lint * update benchmark * [Bugfix] Address PassContext contamination from CI and fix incorrect rewrites in warp specialized pass (#767) * fix ci and pass bug * fix * try * lint * [MXFP4] Add 1D TMA copy for Scale tensor in MXFP4 GEMM (#766) * [TMA] Add 1D TMA copy for Scale tensor * [Lint] * [Test] Add test for kernel * [BugFix] * hot fix blackwell (#768) * [Refactor] Refactor `Operator` into `TileOperator` and with tvm reflection (#763) * Refactor operator classes to inherit from TileOperator and update layout inference methods - Changed base class of several operator classes (AtomicAdd, Copy, Gemm, etc.) from Operator to TileOperator for better alignment with tile operations. - Updated InferLayout and Lower methods to use 'override' specifier for clarity and consistency. - Adjusted header inclusions to replace "op.h" with "operator.h" across multiple files for improved organization. - Added missing layout inference implementations for Fill and Conv2DIm2ColOp. - Removed deprecated op.cc and op.h files to streamline the codebase. * lint fix * Refactor operator classes to use Node pattern and improve memory management - Updated several operator classes (AtomicAdd, Copy, Gemm, etc.) to utilize the Node pattern for better memory management and encapsulation. - Changed constructors to initialize member variables through a node object, enhancing clarity and reducing direct member access. - Updated Clone methods to return TileOperator instances instead of unique pointers, aligning with the new design. - Refactored InferLayout and Lower methods to ensure consistency across operator implementations. - Adjusted header files to reflect the new class structure and removed deprecated code for a cleaner codebase. * Enhance Clone methods in AtomicAdd and Copy classes to support parallel operation cloning - Updated the Clone methods in AtomicAddNode and CopyNode to ensure that the parallel operation (par_op_) is properly cloned when defined, improving the integrity of cloned objects. - Refactored the FillNode class to use ParallelOp directly instead of std::make_unique, streamlining the creation of parallel operations. - Made minor adjustments in layout inference and other related methods for consistency and clarity. * Refactor FillNode::Lower method to remove unused global function call - Eliminated the call to the global function "tl.fill.lower" in the FillNode::Lower method, streamlining the code and improving clarity. - Retained the core functionality of the method while enhancing maintainability by reducing unnecessary dependencies. * [Reducer] Introduce `alloc_reducer` to separate inter and intra warp reduction (#757) * [Enhancement] Introduce finalize_reducer operator and layout reducer support - Added `FinalizeReducer` operator to handle reduction finalization in the TileLang framework, allowing for efficient reduction operations. - Implemented layout inference for local.reducer buffers, enhancing the handling of layout mappings and reducing complexity in buffer management. - Updated `setup.py` to include logging for build directory paths, improving build process visibility. - Enhanced atomic operations with new functions for atomic max, min, load, and store, providing more robust atomicity control in memory operations. - Refactored parallel loop handling to incorporate reducer information, ensuring proper management of reduction operations in parallel contexts. - Cleaned up test cases by removing unnecessary cache disabling and optimizing test parameters for better performance. * Refactor code formatting and improve readability in multiple files - Cleaned up whitespace in `setup.py` to enhance logging clarity. - Reformatted `AtomicMax` and `AtomicMin` functions in `common.h` for better alignment and readability. - Adjusted `debug_print_var` function in `debug.h` to improve code structure and maintainability. - Enhanced readability of the `atomic_add` function in `customize.py` by breaking long lines for better clarity. * Remove debug print statements from `copy.cc` and `inject_tma_barrier.cc` to enhance code clarity and maintainability. * [Enhancement] Disable reuse of small arrays in shared memory allocation - Added logic to prevent the reuse of small arrays (<= 32 bits) in `merge_shared_memory_allocations.cc`, ensuring they are lowered to registers in LLVM for improved performance and memory management. * Refactor `setup.py` to remove duplicate logging statements and enhance clarity. Update `finalize_reducer` function documentation in `reduce.py` to include detailed parameter and return descriptions, improving code readability and maintainability. * Refactor `finalize_reducer` and `reduce` functions to remove redundant target checks. Simplified conditionals by retaining only the `TargetIsHopper` check, enhancing code clarity and maintainability. * bug fix * Add thread checks workaround for replicated cases * Remove the is_one check * fix lint error * lint fix * Update autotune tests to use smaller matrix sizes for improved performance and reliability * [Refactor] Update FinalizeReducer to FinalizeReducerOp and adjust related methods - Refactored FinalizeReducer class to FinalizeReducerOp, updating constructor and method signatures for consistency with the new TileOperator structure. - Enhanced layout inference and cloning methods in FinalizeReducerOpNode. - Updated test_example_flash_attention.py to call test_example_gqa_bwd instead of tilelang.testing.main. - Adjusted header inclusions for improved organization and clarity across multiple files. * [Refactor] Update atomic operations in common.h and modify test_example_flash_attention.py - Enhanced atomic operations (Add, Min, Max) in common.h to handle half and bfloat16 types more efficiently. - Updated test_example_flash_attention.py to call test_example_gqa_bwd instead of tilelang.testing.main, improving test organization. * [Refactor] Simplify CopyNode::LowerBulkCopy logic and update test execution - Removed redundant checks for contiguous memory access in CopyNode::LowerBulkCopy, streamlining the logic for TMA copy operations. - Updated test_tilelang_kernel_gemm.py to comment out the main testing function and call a specific test for i8i8i32 tensor operations instead, improving test focus. --------- Co-authored-by: Huanqi Cao <caohuanqi@deepseek.com> Co-authored-by: Freebase6912 <amid-gauze-racing@duck.com> * 📝 Add docstrings to `pytile_0826` (#770) * 📝 Add docstrings to `pytile_0826` Docstrings generation was requested by @LeiWang1999. * https://github.com/tile-ai/tilelang/pull/763#issuecomment-3224197814 The following files were modified: * `src/op/atomic_add.cc` * `src/op/atomic_add.h` * `src/op/copy.cc` * `src/op/copy.h` * `src/op/elem.cc` * `src/op/elem.h` * `src/op/gemm.cc` * `src/op/gemm.h` * `src/op/gemm_sp.cc` * `src/op/gemm_sp.h` * `src/op/operator.cc` * `src/op/operator.h` * `src/op/parallel.cc` * `src/op/parallel.h` * `src/op/reduce.cc` * `src/op/reduce.h` * `src/op/region.cc` * `src/op/region.h` * `src/transform/layout_inference.cc` * `src/transform/lower_tile_op.cc` * lint fix --------- Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> * [Bugfix]:Fix atomic add auto vectorize negative optimization (#765) * [Bugfix]:Fix atomic add auto vectorize negative optimization * fixbug * format * fix bug * 📝 Add docstrings to `reducer_0825` (#772) * 📝 Add docstrings to `reducer_0825` Docstrings generation was requested by @LeiWang1999. * https://github.com/tile-ai/tilelang/pull/757#issuecomment-3219088118 The following files were modified: * `setup.py` * `src/op/builtin.h` * `src/op/finalize_reducer.cc` * `src/op/finalize_reducer.h` * `src/op/parallel.cc` * `src/op/parallel.h` * `src/op/reduce.cc` * `src/target/codegen_cuda.cc` * `src/tl_templates/cuda/common.h` * `src/transform/layout_inference.cc` * `src/transform/layout_reducer.cc` * `src/transform/layout_reducer.h` * `src/transform/merge_shared_memory_allocations.cc` * `src/transform/storage_access.cc` * `src/transform/warp_specialized_rewriter.cc` * `testing/python/autotune/test_tilelang_autotune_with_inputs.py` * `tilelang/engine/phase.py` * `tilelang/language/customize.py` * `tilelang/language/reduce.py` * `tilelang/transform/__init__.py` * lint fix * lint fix --------- Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> * Allow fill global buffer (#774) * Allow fill global buffer * fix lint error * [BugFix] Refactor the op check in LowerTileOp pass using the member function instead of string match (#771) * [BugFix] Refactor the op check in LowerTileOp pass using the member function instead of string match * [Lint] * add bf16 exp fallback (#776) * [Lint] Introduce clang-tidy into format.sh (#777) * [Refactor] Update Clang-Tidy Checks and Improve Code Consistency - Enhanced .clang-tidy configuration by adding specific checks for better bug detection and performance optimization. - Refactored function signatures across multiple files to use `const` references for parameters, improving performance and code clarity. - Updated various methods to ensure consistent handling of parameters, particularly in `AddPredicate`, `Substitute`, and `PlanLoopPartition` functions. - Improved readability by replacing size checks with `empty()` method calls in several locations, ensuring clearer intent in the code. - General code cleanup and adherence to best practices for better maintainability. * [Refactor] Enhance Code Consistency and Clang-Tidy Configuration - Updated .clang-tidy configuration to include additional checks for improved code quality and performance. - Refactored function signatures across multiple files to use `const` references, enhancing performance and clarity. - Replaced size checks with `empty()` method calls in various locations for clearer intent. - Improved handling of parameters in several functions, ensuring consistent usage of `std::move` where applicable. - General code cleanup to adhere to best practices and improve maintainability. * [Refactor] Integrate Clang-Tidy Checks and Enhance Code Consistency - Added clang-tidy checks to the format script for improved code quality assurance. - Refactored function signatures across multiple files to consistently use `const` references, enhancing performance and clarity. - Updated the requirements-lint.txt file to include clang-tidy as a dependency. - General code cleanup to adhere to best practices and improve maintainability. * [CI] Update AMD CI Workflow to Include Build Directory Creation - Added steps to create a build directory and configure CMake with ROCm support during the format check process. - Ensured cleanup of the build directory after the format check to maintain a clean workspace. * [Refactor] Remove Unused Member Variables in AtomicAddNode and CopyNode - Removed the `args_` member variable from both `AtomicAddNode` and `CopyNode` classes to streamline the code and eliminate unnecessary data members. - This change enhances code clarity and maintainability by focusing on relevant attributes for each class. * [Refactor] Update Clang-Tidy Integration and Code Improvements - Modified the format script to include the `-fix` option in the clang-tidy command for automatic code fixes. - Refactored the `AtomicAddVectorizePlanner` class to improve variable handling and consistency, including changes to member variable types and function signatures. - Enhanced code clarity by removing unnecessary `std::move` calls and ensuring consistent usage of types across the class. - General code cleanup to adhere to best practices and improve maintainability. * [Refactor] Improve Parameter Handling and Consistency in AtomicAddVectorize - Updated function signatures in `AtomicAddVectorizePlanResult` and `AtomicAddVectorizeRewriter` to use `const` references and `std::move` for better performance and clarity. - Enhanced the `UpdateVectorSize` method to accept `const Array<PrimExpr>&` for improved efficiency. - General code cleanup to maintain consistency and adhere to best practices. * [CI] Add Git Submodule Initialization to CI Workflow - Included a step to initialize and update git submodules recursively in the CI workflow. - This change ensures that all necessary submodules are available during the format check process, improving build reliability. * [CI] Add Git Submodule Update Step to Format Check - Included a command to initialize and update git submodules recursively in the CI workflow during the format check process. - This enhancement ensures that all required submodules are available, contributing to improved build reliability. * [Refactor] Update Function Signatures in AtomicAddVectorize - Modified the `VectorizeAtomicAdd` function signature to use `const` references for `thread_var` and `thread_bounds`, enhancing performance and code clarity. - This change aligns with previous refactoring efforts to improve parameter handling and consistency across the codebase. * [Cache] Introduce detailed target information for the disk kernel cache (#780) * Fix type hint for target_host parameter in compile function to allow None value * Refactor target handling in compile function to utilize determine_target for improved clarity and consistency * Update PrintConst function in codegen_cuda.cc to use hexfloat format for bfloat16 and float8/float4 types, while adding scientific notation comments for clarity. This change enhances the representation of floating-point constants in the generated code. * Refactor PrintType function in codegen_cuda.cc to remove unnecessary failure conditions for floating-point types with lane counts greater than 4. This change simplifies the logic and improves code clarity. * Enhance benchmark_matmul.py to conditionally print Reference TFlops only if ref_latency is not None. Update param.py to ensure target is converted to string for consistency. Refactor tuner.py to utilize determine_target for improved clarity in target handling. * Remove automatic commit and push step from AMD and NVIDIA CI workflows to streamline the process and avoid unnecessary commits. * [Example]Adds example for top-k operation (#775) * [Example]Adds example for top-k operation Adds an example demonstrating the top-k operation using tilelang * format * Adds topk tilelang example test * fix lint * [Math] Dispatch `T.rsqrt(x)` into cuda intrin instead of `1 / T.sqrt(x)` (#781) * Fix type hint for target_host parameter in compile function to allow None value * Refactor target handling in compile function to utilize determine_target for improved clarity and consistency * Update PrintConst function in codegen_cuda.cc to use hexfloat format for bfloat16 and float8/float4 types, while adding scientific notation comments for clarity. This change enhances the representation of floating-point constants in the generated code. * Refactor PrintType function in codegen_cuda.cc to remove unnecessary failure conditions for floating-point types with lane counts greater than 4. This change simplifies the logic and improves code clarity. * Enhance benchmark_matmul.py to conditionally print Reference TFlops only if ref_latency is not None. Update param.py to ensure target is converted to string for consistency. Refactor tuner.py to utilize determine_target for improved clarity in target handling. * Remove automatic commit and push step from AMD and NVIDIA CI workflows to streamline the process and avoid unnecessary commits. * Add intrin_rule source files to CMakeLists.txt and implement hrsqrt function for half_t in common.h * lint fix * remove cmake dep in pyproject as it may lead to different cmake paths in diff stages * lint fix * Add cmake dependency to pyproject.toml and improve build logging in setup.py * [CI] Adds pytest-durations for test timing (#782) * [Ci] Adds pytest-durations for test timing Adds `pytest-durations` to the test requirements and configures pytest to display test durations. This helps in identifying slow-running tests and optimizing the test suite for faster feedback. * add amd ci durations * Removes flash_attn installation from CI * [Refactor] Support python reflection for tile operators (#783) * Implement Fill operator and related reflection methods in TileLang - Added Fill operator implementation in `fill.cc` and `fill.h` for element-wise filling of buffers. - Introduced reflection methods for Fill, AtomicAdd, Copy, Conv2DIm2Col, FinalizeReducer, Gemm, and Parallel operators to enhance introspection capabilities. - Updated relevant files to register reflection methods and ensure proper initialization in static blocks. - Removed outdated comments and unnecessary code in various operator files to improve clarity and maintainability. - Added new Python bindings for the Fill operator in `tilelang/ir/fill.py` and updated the module imports accordingly. * Refactor operator reflection methods and improve code clarity - Updated reflection methods for AtomicAdd, Copy, FinalizeReducer, Gemm, and Parallel operators to enhance readability by using `empty()` instead of size checks. - Consolidated static initialization blocks for various operators to a single line for improved consistency. - Cleaned up whitespace and formatting in multiple files to adhere to coding standards and improve maintainability. - Added new Python bindings for operators in the `tilelang/ir` module, ensuring proper registration and organization of imports. * Refactor GEMM and AtomicAdd operations for improved clarity - Updated the `GetArchInt` function in `atomic_add.cc` to use `std::string` and `std::stoi` for better readability and type safety. - Removed unnecessary variables and comments in `gemm_sp.cc` and `gemm.cc` to streamline the `ComputeWarpPartition` method. - Cleaned up the `layout_reducer.cc` file by removing unused variable declarations, enhancing code clarity. - Added import for the `ir` module in `tilelang/__init__.py` to ensure proper organization of module imports. * Remove deprecated operator files from the tilelang IR module - Deleted files for Fill, AtomicAdd, Copy, Gemm, GemmSP, FinalizeReducer, Parallel, Reduce, and Region operators to streamline the codebase. - This cleanup enhances maintainability by removing unused code and improving overall organization of the module. * Refactor imports in tilelang IR module for improved organization - Updated import statements in `tilelang/ir.py` to reflect changes in the TVM library structure, enhancing clarity and maintainability of the codebase. * lint fix * Refactor GEMM and GEMM-SP operations to enhance clarity and maintainability - Updated the `Gemm` and `GemmSP` classes to utilize a new `GemmWarpPolicy` object for warp partitioning, improving encapsulation and readability. - Removed deprecated `ComputeWarpPartition` methods and replaced them with calls to the new policy object, streamlining the code. - Cleaned up comments and unnecessary code in `gemm.cc`, `gemm_sp.cc`, and related header files to enhance overall clarity. - Introduced a new `GemmWarpPolicyNode` class to manage warp policy attributes and methods, facilitating better organization of related functionalities. - Updated reflection methods to include the new policy structure, ensuring proper registration and introspection capabilities. * Refactor Reduce operation to utilize ReduceType class for improved clarity and maintainability - Replaced multiple conditional checks for reduce types with a single ReduceType object, simplifying the code structure. - Introduced a new ReduceTypeNode class to encapsulate reduce type logic and methods, enhancing organization. - Updated MakeInitValue, MakeReduce, and Lower methods to leverage the new ReduceType class, improving readability. - Added Python bindings for the ReduceType class in tilelang IR module to ensure proper registration and usability. * comment * Refactor operator header files for improved readability - Cleaned up formatting and whitespace in `atomic_add.h`, `copy.h`, `fill.h`, `reduce.cc`, and `reduce.h` to enhance code clarity. - Consolidated comments and adjusted line breaks for better organization and maintainability across multiple operator definitions. * Refactor MakeReduce method in ReduceOpNode for clarity - Updated the parameter name in the MakeReduce method from `rhs` to `b` and assigned it to `rhs` for improved readability. - This change enhances the clarity of the method's purpose and aligns with the overall refactoring efforts in the Reduce operation. * Update Reduce operation type checks for consistency - Changed string comparisons for reduce types in the MakeReduce method from "abs_sum" to "abssum" and "abs_max" to "absmax" for uniformity. - This adjustment enhances the clarity and consistency of the reduce type handling in the codebase. * [AMD] Fix amd tir&add examples (#784) * [Enhancement] Refactor buffer index handling for improved precision and clarity (#668) - Enhanced buffer index handling to address precision issues by removing redundant operations. - Streamlined the logic for determining buffer overlaps, ensuring more accurate conflict detection. - Updated related documentation to reflect changes in buffer management practices. * Remove obsolete test script for AMD example, streamlining the examples directory. * Remove unused dtype_size variable in AMD example script to streamline code. * Add input configuration file and update AMD example script for enhanced flexibility - Introduced a new input.txt file for configurable parameters. - Modified the example_amd_flash_attn_fwd.py script to allow for a wider range of configurations, including additional options for num_stages, enable_rasterization, and k_pack. - Streamlined the main function for better clarity and organization. - Added a new test script to facilitate running the example with specified parameters. * Remove input configuration file and obsolete test script; enhance AMD example with swizzle layout annotations - Deleted input.txt and test.sh files as they are no longer needed. - Updated example_amd_flash_attn_fwd.py to include swizzle layout annotations for shared memory, improving bank conflict avoidance. - Reintroduced swizzle usage in the kernel for better performance. * Refactor AMD example script for FlashAttention-2 - Updated function names for clarity, changing `get_v2_configs` to `get_configs` and `fast_flashattn_v2` to `fast_flashattn`. - Streamlined the main function by renaming `main_v2` to `main` and adjusting the corresponding calls. - Removed outdated comments and improved code organization for better readability. * Refactor formatting in AMD FlashAttention example script - Improved code readability by adjusting line breaks and indentation in the `fast_flashattn` function. - Streamlined the `main` function parameter formatting for consistency. - Removed unnecessary blank lines to enhance overall code organization. * Update example_amd_flash_attn_fwd.py * Enhance AMD example script and update CI workflows - Improved the `example_amd_flash_attn_fwd.py` script for better clarity and organization. - Added new CI workflows for AMD and documentation publishing. - Updated various requirements files to include necessary dependencies. - Introduced new test cases and examples for better coverage and functionality. - Refactored existing code for improved readability and maintainability. * Remove redundant tool cache cleanup step in AMD CI workflow * Remove `torch` dependency from `requirements-rocm.txt` to streamline requirements. * Add new AMD FlashAttention example and test script - Introduced `example_amd_flash_attn_bwd.py` for backward attention computation using TileLang. - Added `test.sh` script to facilitate running the new example with specified parameters. - Enhanced the overall structure and organization of the example for better clarity and usability. * Update configurations in `example_amd_flash_attn_fwd.py` for autotuner - Reduced the number of threads and `num_split_q` options for improved performance. - Adjusted `panel_size` options to streamline configuration settings. * Update submodule 'tvm' to commit 6ccc74f622c7ec4ac25d430d0f6546e7b9edb217 * Update submodule 'tvm' to commit 14ff70ab142b9e5a31bbf9c7923c8a697d41e86c * Add example for AMD Flash Attention backward pass implementation - Introduced a new example script `example_amd_flash_attn_bwd.py` demonstrating the forward and backward operations of Flash Attention using TileLang. - Implemented JIT-compiled functions for both forward and backward passes, including preprocessing and postprocessing steps. - Added a main function to facilitate testing and benchmarking of the attention mechanism with configurable parameters. - Included reference implementation for validation against PyTorch's attention mechanism. This addition enhances the examples directory by providing a comprehensive guide for users to understand and utilize Flash Attention in their applications. * Enhance AMD Flash Attention example with additional testing capabilities - Updated `example_amd_flash_attn_bwd.py` to include more comprehensive testing features for the Flash Attention implementation. - Improved the main function to allow for better parameter configuration and benchmarking. - Added validation checks against PyTorch's attention mechanism to ensure accuracy and reliability of the example. This update aims to provide users with a more robust tool for understanding and utilizing Flash Attention in their applications. * Update submodule TVM to commit a64a5926a6e59f5417ef2501f9d88b467337cf6a * Refactor HIP intrinsic rules to CUDA - Updated file name from `intrin_rule_hip.cc` to `intrin_rule_cuda.cc` to reflect the change in focus from HIP to CUDA intrinsic rules. - Adjusted include paths for better organization and clarity in the code structure. * Update AMD CI workflow to uninstall specific PyTorch packages before installation - Removed the installation of `flash_attn==2.5.8` to streamline the CI process. - Added a step to uninstall `torch`, `torchvision`, and `torchaudio` prior to installing pre-release versions, ensuring compatibility and reducing potential conflicts. * Remove unused shared memory allocations in AMD Flash Attention backward example - Eliminated the allocation of shared memory for `dv_shared` and `dk_shared` in `example_amd_flash_attn_bwd.py` to streamline memory usage and improve performance. - This change focuses on optimizing the backward pass implementation by reducing unnecessary memory overhead. * Remove unnecessary pip uninstall command from AMD CI workflow - Eliminated the step to uninstall `torch`, `torchvision`, and `torchaudio` in the AMD CI workflow, as it is no longer required for the installation of pre-release versions. - This change simplifies the CI process and reduces potential overhead during package management. * Refactor DispatchHIPWarpActiveMask function in HIP intrinsic rules - Updated the return statement to use std::string for concatenation in the case of 16-bit types, improving code clarity. - Added a null check for the CallNode pointer in DispatchHIPWarpActiveMask to enhance robustness and prevent potential dereferencing issues. * Refactor formatting of HIP intrinsic rule registrations - Adjusted the formatting of TVM_REGISTER_OP calls for better readability by aligning method chaining. - No functional changes were made; this update focuses on code style improvements to enhance maintainability. * Update file na…
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As Title
Summary by CodeRabbit
New Features
Public API
Tests